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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML) for applications in
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] Subject Area: Statistics and Machine Learning Appl Deadline: 2025/12/01 11:59PM (posted 2025/09/04, listed until 2026/02/01) Position Description: Apply Position Description The Department of Mathematics
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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar
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. The postdoctoral researcher will conduct cutting-edge research in areas such as cyber-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position will involve
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of machine learning researchers, engineers, and domain scientists. This collaborative environment offers a unique opportunity to work on cutting edge AI models, with a focus on generalization and
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science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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) Experience in genomics, single-cell data, or machine learning (preferred) Why this is exceptional Build next-generation AI models of the human genome Work with one of the richest longitudinal PD datasets
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics
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-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position will involve collaborating with faculty and graduate students on interdisciplinary research